Regression is a process of estimating the variable relationships. It includes several techniques for analysing and modelling variables, especially on the relationship between one or more independent variables, and dependent variables.
We have thoroughly discussed regression and its processes on the previous posts. Now, it is time to learn how to write a regression equation using spss. We have SPSS regression tutorials that provide insights on the stepbystep procedure of performing linear regression using the SPSS Data Editor Verison 12.0. Through this version, identify the writing regression equation. You can also check manova spss output interpretation or how to write interpretation to get more info.
SPSS Regression Requirements and Processes
Writing the regression equation using SPSS is unachievable without the tools. Download the standard class data set and open it.
As we all know, linear regression describes the relationship between variables. Working with the SPSS tool helps in compressing time while creating value to the linear regression equation.

To start the equation, open the SPSS Data editor and go to Analyse> Regression> Linear.

The Linear Regression box appears.

Click the left hand pane of the box to choose the variable you want to calculate. To transfer the variable into the Dependent box, click the top arrow button.

Click Linear Regression dialog box’s left hand pane to select the single variable that you want prediction. If you select more variable, you’re performing multiple regressions instead of linear regression. This is mostly reserved for graduate schools.

Click the arrow button to transfer the data on the Independent’s box.

By clicking the Statistics button, SPSS will provide the descriptive statistics of the dependent and independent variables. The Statistics Dialog Box will pop out.

Select it by clicking the box next to the Descriptives.

Select Continue Button.

Perform the regression by clicking OK in the Linear Regression dialog box.
SPSS Regression Output
The mean, observation count, for each independent and dependent variables, and standard deviation is provided by the Descriptive Statistics.
The correlation coefficients are showed by the Correlations, which are differently organized from the correlation procedure. The correlation between dependent and independent variables is shown in the first row, while the correlation coefficient’s significance is given by the next row. The last row shows the variable’s number of observations.
Multiple Regression is mostly used on the Model Summary Part, but since we are discussing linear regression, we don’t need to touch it. The multiple correlation coefficient, which is the capital R, shows us the strong levels of the relationship between multiple independent variables to the dependent variables.
R= r stands for multiple correlations is equal to the bivariate correlation’s absolute value. The coefficient of determination is provided the R square.
The ANOVA part is not useful for some purposes. It indicates the regression equation that statistically explains the significant portion of the dependent variable’s variability from the independent variable’s variability.
The Coefficient part provides the values necessary to write the regression equation in the form of:
Predicated variable (dependent variable) = slope* independent variable + intercept